Monthly model - Regions
Tanya Rogers
1/6/2022
library(dplyr)
#library(psych) #for pairs.panels, but could use other packages, e.g. GGalley
library(lavaan)
library(semPlot)
library(DiagrammeR)
library(ggplot2)
library(tidyr)Import data
combined=read.csv("data/monthly_averages/monthly_data_compiled_regions.csv",stringsAsFactors = F)
cnames=read.csv("analysis/column_names_region_monthly.csv", stringsAsFactors = F)
dsub=filter(combined, Year>=1995) %>% arrange(Region,Year,Month)
focaldata=dsub[,cnames$Datacolumn]
fvars=cnames$Shortname
colnames(focaldata)=fvars
regions=unique(focaldata$region)
regionorder=c("Far West","West","North","South")
focaldata=focaldata%>%
mutate(decyear=year+(month-1)/12,
sinmon=sin(2*pi/12*month),
cosmon=cos(2*pi/12*month))
#focal variables
varnames=c("temp", "flow","nitrate","ammonia","dophos","chla","hcope","clad","amphi","pcope","mysid","potam","corbic","sside","estfish_bsot","estfish_bsmt")
#labels for lagged vars
cnameslag=cnames
cnameslag$Shortname=paste0(cnameslag$Shortname,"_1")
cnameslag$Diagramname=paste(cnameslag$Diagramname,"(t-1)")
cnameslag=rbind(cnames,cnameslag)
source("analysis/myLavaanPlot.r")Notes:
Determine which vars are appropriate for which regions and at what lags.
Write out path diagram for each region.
Model with and without fish, probably, because of missing data.
Data prep
Log transform, scale.
Within and across regions.
Create set with regional monthly means removed.
#log transform
logvars=fvars[cnames$Log=="yes"]
logtrans=function(x) {
x2=x[which(!is.na(x))]
if(any(x2==0)) {log(x+min(x2[which(x2>0)],na.rm=T))}
else {log(x)}
}
focaldatalog = focaldata %>%
mutate_at(logvars,logtrans)
#scale data
fdr0=focaldatalog
tvars=fvars[-(1:3)]
#scaled within regions
fdr=fdr0 %>%
group_by(region) %>%
#scale
mutate_at(tvars,scale) %>%
#lag
mutate_at(tvars,list("1"=lag,"2"=function(x) {lag(x,2)})) %>%
ungroup() %>%
as.data.frame()
#scaled within regions, remove monthly means
fdr_ds=fdr %>%
group_by(region,month) %>%
mutate_at(tvars,list("mm"=function(x) {mean(x,na.rm = T)})) %>%
mutate_at(tvars,function(x) {x-mean(x,na.rm = T)}) %>%
ungroup() %>%
#lag
group_by(region) %>%
mutate_at(tvars,list("1"=lag,"2"=function(x) {lag(x,2)})) %>%
ungroup() %>%
as.data.frame()
#scaled across regions
fdr1=fdr0 %>%
#scale
mutate_at(tvars,scale) %>%
#lag
group_by(region) %>%
mutate_at(tvars,list("1"=lag,"2"=function(x) {lag(x,2)})) %>%
ungroup() %>%
as.data.frame()
#scaled across regions, monthly means removed
fdr1_ds=fdr1 %>%
group_by(region,month) %>%
mutate_at(tvars,list("mm"=function(x) {mean(x,na.rm = T)})) %>%
mutate_at(tvars,function(x) {x-mean(x,na.rm = T)}) %>%
ungroup() %>%
#lag
group_by(region) %>%
mutate_at(tvars,list("1"=lag,"2"=function(x) {lag(x,2)})) %>%
ungroup() %>%
as.data.frame()Data availability by series and region (prop zeros and prop missing)
FW: ignore clad, corbic, sside
W: ignore corbic, sside
S: ignore potam
dataavail=focaldata %>%
gather(var, value, 4:length(fvars)) %>%
group_by(region, var) %>%
summarize(
propmissing=length(which(is.na(value)))/length(value),
propzeros=length(which(value==0))/length(which(!is.na(value)))) %>%
as.data.frame()
#these variables should not be used (too many zeros)
filter(dataavail,propzeros>0.5 | is.na(propzeros))## region var propmissing propzeros
## 1 Far West clad 0.1382637 0.9514925
## 2 Far West corbic 0.1382637 1.0000000
## 3 Far West marfish_bsot 0.4565916 0.6508876
## 4 Far West sbass_bsmt 0.4951768 0.7515924
## 5 Far West sbass_bsot 0.4565916 0.6982249
## 6 Far West smelt_bsmt 0.4951768 0.9808917
## 7 Far West smelt_bsot 0.4565916 0.9940828
## 8 Far West sside 1.0000000 NaN
## 9 North lfin_bsmt 0.5144695 0.8741722
## 10 North lfin_bsot 0.4694534 0.9393939
## 11 North marfish_bsmt 0.5144695 0.9735099
## 12 North marfish_bsot 0.4694534 0.9939394
## 13 North smelt_bsot 0.4694534 0.8242424
## 14 South lfin_bsmt 0.5128205 1.0000000
## 15 South lfin_bsot 0.4679487 1.0000000
## 16 South marfish_bsmt 0.5128205 1.0000000
## 17 South marfish_bsot 0.4679487 1.0000000
## 18 South potam 0.1378205 0.9702602
## 19 South sbass_bsmt 0.5128205 0.6118421
## 20 South smelt_bsmt 0.5128205 0.8486842
## 21 South smelt_bsot 0.4679487 0.9156627
## 22 West corbic 0.1350482 0.8066914
## 23 West marfish_bsot 0.4598071 0.9047619
## 24 West smelt_bsmt 0.4983923 0.5128205
## 25 West smelt_bsot 0.4598071 0.7619048
## 26 West sside 1.0000000 NaN#these variables have lots of missing data
filter(dataavail,propmissing>0.45 | is.na(propmissing))## region var propmissing propzeros
## 1 Far West estfish_bsmt 0.4951768 0.165605096
## 2 Far West estfish_bsot 0.4565916 0.201183432
## 3 Far West lfin_bsmt 0.4951768 0.356687898
## 4 Far West lfin_bsot 0.4565916 0.248520710
## 5 Far West marfish_bsmt 0.4951768 0.006369427
## 6 Far West marfish_bsot 0.4565916 0.650887574
## 7 Far West sbass_bsmt 0.4951768 0.751592357
## 8 Far West sbass_bsot 0.4565916 0.698224852
## 9 Far West smelt_bsmt 0.4951768 0.980891720
## 10 Far West smelt_bsot 0.4565916 0.994082840
## 11 Far West sside 1.0000000 NaN
## 12 North estfish_bsmt 0.5176849 0.126666667
## 13 North estfish_bsot 0.4694534 0.109090909
## 14 North lfin_bsmt 0.5144695 0.874172185
## 15 North lfin_bsot 0.4694534 0.939393939
## 16 North marfish_bsmt 0.5144695 0.973509934
## 17 North marfish_bsot 0.4694534 0.993939394
## 18 North sbass_bsmt 0.5176849 0.480000000
## 19 North sbass_bsot 0.4694534 0.133333333
## 20 North smelt_bsmt 0.5144695 0.403973510
## 21 North smelt_bsot 0.4694534 0.824242424
## 22 South estfish_bsmt 0.5128205 0.177631579
## 23 South estfish_bsot 0.4679487 0.006024096
## 24 South lfin_bsmt 0.5128205 1.000000000
## 25 South lfin_bsot 0.4679487 1.000000000
## 26 South marfish_bsmt 0.5128205 1.000000000
## 27 South marfish_bsot 0.4679487 1.000000000
## 28 South sbass_bsmt 0.5128205 0.611842105
## 29 South sbass_bsot 0.4679487 0.012048193
## 30 South smelt_bsmt 0.5128205 0.848684211
## 31 South smelt_bsot 0.4679487 0.915662651
## 32 West estfish_bsmt 0.4983923 0.019230769
## 33 West estfish_bsot 0.4598071 0.017857143
## 34 West lfin_bsmt 0.4983923 0.250000000
## 35 West lfin_bsot 0.4598071 0.392857143
## 36 West marfish_bsmt 0.4983923 0.115384615
## 37 West marfish_bsot 0.4598071 0.904761905
## 38 West sbass_bsmt 0.4983923 0.141025641
## 39 West sbass_bsot 0.4598071 0.035714286
## 40 West smelt_bsmt 0.4983923 0.512820513
## 41 West smelt_bsot 0.4598071 0.761904762
## 42 West sside 1.0000000 NaNTime series plots
Cross-correlation matrices
(only sig correlations shown… no correction for multiple comparisons)
Other notes:
Detrended fish indices are NOT correlated in S!
Nitrate and ammonia are positively correlated, max at lag 0 all regions.
Nitrate and dophos are positively correlated, max at lag 0 all regions.
Ammonia and dophos are positively correlated, lag 0 for FW and S, ammonia lags dphos by 3 months in W and N.
Chla nitrate neg correlated, lag 0.
Chla ammonia neg correlated, lag 0.
Chla dophos relationship unclear.
High flow 2-4 month prev = high chla
Hcope lags chla by 1, positive, except FW.
Clad seem to precede chla by 2, positive.
Amphi relationship unclear, prob bc not eating chla in water column.
In N and W, chla lags potam, negative. The opposite in W.
Mysid and hcope postive, lag 0.
In S and W, hcope lags pcope, negative.
Exploratory plots
SEM model
Note: You get somewhat different answers if you use a multigroup model vs. fit each region individually. I am not entirely sure why, but I think there is some data pooling going on like in hierarchical models. I would have to look into the math. I know that if you use the multigroup framework you can impose constraints like requiring certain parameters to be the same across groups (see lavaan manual). Obviously, if data on a variable you want to use are missing from one site, you can’t use the multigroup option (you will get an error) and have to fit individually.
I haven’t looked in to how to modify the new (nicer) plotting code to work with the lags and multiple groups, but that is something that can be done.
I haven’t added corbic and sside, but that is something to explore.
Chla, nutrients, flow
Clams don’t seem to have any impact on chla. Dropping dophos because strongly correlated with ammonia.
# mod1='chla~ammonia+dophos+chla_1+flow_1
# ammonia~flow_1+chla_1
# dophos~flow_1
# ammonia~~dophos'
mod1='chla~ammonia+chla_1+flow_1
ammonia~flow_1+chla_1'
#you can either do a multigroup model by region
# modfit1=sem(mod1, data=fdr_ds, group = "region")
# summary(modfit1, standardized=T, rsq=T)
#
# par(mfrow=c(2,2))
# semPaths(modfit1, "std", edge.label.cex = 1, residuals = F, node.width=3, nCharNodes = 0, intercepts = F, title = T)
# #residuals(modfit1)
#or fit separately for each region
modfitFW=sem(mod1, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(mod1, data=filter(fdr_ds,region=="West"))
modfitN=sem(mod1, data=filter(fdr_ds,region=="North"))
modfitS=sem(mod1, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 13 iterations
##
## Optimization method NLMINB
## Number of free parameters 7
##
## Used Total
## Number of observations 297 311
##
## Estimator ML
## Model Fit Test Statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## ammonia -0.301 0.050 -5.982 0.000 -0.301 -0.315
## chla_1 0.314 0.052 6.028 0.000 0.314 0.317
## flow_1 -0.086 0.054 -1.605 0.108 -0.086 -0.085
## ammonia ~
## flow_1 -0.165 0.061 -2.690 0.007 -0.165 -0.156
## chla_1 0.022 0.060 0.363 0.717 0.022 0.021
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.571 0.047 12.186 0.000 0.571 0.803
## .ammonia 0.762 0.063 12.186 0.000 0.762 0.974
##
## R-Square:
## Estimate
## chla 0.197
## ammonia 0.026summary(modfitW, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 16 iterations
##
## Optimization method NLMINB
## Number of free parameters 7
##
## Used Total
## Number of observations 304 311
##
## Estimator ML
## Model Fit Test Statistic 0.000
## Degrees of freedom 0
## Minimum Function Value 0.0000000000000
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## ammonia -0.194 0.057 -3.408 0.001 -0.194 -0.206
## chla_1 0.340 0.053 6.383 0.000 0.340 0.341
## flow_1 -0.002 0.051 -0.033 0.974 -0.002 -0.002
## ammonia ~
## flow_1 -0.426 0.045 -9.407 0.000 -0.426 -0.474
## chla_1 0.110 0.053 2.064 0.039 0.110 0.104
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.422 0.034 12.329 0.000 0.422 0.853
## .ammonia 0.427 0.035 12.329 0.000 0.427 0.770
##
## R-Square:
## Estimate
## chla 0.147
## ammonia 0.230summary(modfitN, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 14 iterations
##
## Optimization method NLMINB
## Number of free parameters 7
##
## Used Total
## Number of observations 304 311
##
## Estimator ML
## Model Fit Test Statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## ammonia -0.235 0.061 -3.846 0.000 -0.235 -0.222
## chla_1 0.218 0.055 3.971 0.000 0.218 0.218
## flow_1 -0.051 0.056 -0.901 0.368 -0.051 -0.052
## ammonia ~
## flow_1 -0.293 0.050 -5.840 0.000 -0.293 -0.317
## chla_1 0.047 0.051 0.915 0.360 0.047 0.050
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.594 0.048 12.329 0.000 0.594 0.912
## .ammonia 0.524 0.043 12.329 0.000 0.524 0.897
##
## R-Square:
## Estimate
## chla 0.088
## ammonia 0.103summary(modfitS, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 14 iterations
##
## Optimization method NLMINB
## Number of free parameters 7
##
## Used Total
## Number of observations 303 312
##
## Estimator ML
## Model Fit Test Statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## ammonia -0.315 0.055 -5.697 0.000 -0.315 -0.282
## chla_1 0.397 0.050 7.867 0.000 0.397 0.399
## flow_1 -0.171 0.049 -3.520 0.000 -0.171 -0.177
## ammonia ~
## flow_1 -0.013 0.051 -0.258 0.797 -0.013 -0.015
## chla_1 0.108 0.052 2.071 0.038 0.108 0.121
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.484 0.039 12.309 0.000 0.484 0.732
## .ammonia 0.523 0.042 12.309 0.000 0.523 0.984
##
## R-Square:
## Estimate
## chla 0.268
## ammonia 0.016labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#FAR WEST
myLavaanPlot(model=modfitFW, labels=labelsnorth,
node_options=list(shape="box", fontname="Helvetica"),
coefs=TRUE, stand=TRUE, covs=TRUE, sig=0.05,
width=c("regress","latent","covs"),
color=c("regress","latent","covs"))#WEST
myLavaanPlot(model=modfitW, labels=labelsnorth,
node_options=list(shape="box", fontname="Helvetica"),
coefs=TRUE, stand=TRUE, covs=TRUE, sig=0.05,
width=c("regress","latent","covs"),
color=c("regress","latent","covs"))#NORTH
myLavaanPlot(model=modfitN, labels=labelsnorth,
node_options=list(shape="box", fontname="Helvetica"),
coefs=TRUE, stand=TRUE, covs=TRUE, sig=0.05,
width=c("regress","latent","covs"),
color=c("regress","latent","covs"))#SOUTH
myLavaanPlot(model=modfitS, labels=labelsnorth,
node_options=list(shape="box", fontname="Helvetica"),
coefs=TRUE, stand=TRUE, covs=TRUE, sig=0.05,
width=c("regress","latent","covs"),
color=c("regress","latent","covs"))# par(mfrow=c(2,2))
# semPaths(modfitFW, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("Far West", line = 1)
# semPaths(modfitW, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("West", line = 1)
# semPaths(modfitN, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("North", line = 1)
# semPaths(modfitS, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("South", line = 1)
#residuals(modfitFW)Chla, zooplankton
This definitely needs work.
modFW='chla~chla_1+hcope_1+amphi_1+flow_1
amphi~chla_1+amphi_1+flow_1
hcope~chla_1+hcope_1+flow_1+pcope_1
pcope~chla_1+pcope_1+hcope_1+amphi_1+flow_1
'
modW='chla~chla_1+hcope_1+flow_1
hcope~chla_1+hcope_1+mysid_1+pcope_1+flow_1
pcope~chla_1+pcope_1+hcope_1+mysid_1+flow_1
mysid~chla_1+mysid_1+hcope_1+pcope_1+flow_1
'
modN='chla~chla_1+hcope_1+amphi_1+flow_1
amphi~chla_1+amphi_1+flow_1
hcope~chla_1+hcope_1+flow_1+pcope_1
pcope~chla_1+pcope_1+hcope_1+flow_1
'
modS='chla~chla_1+hcope_1+clad_1+flow_1
clad~chla_1+clad_1+flow_1+pcope_1
hcope~chla_1+hcope_1+flow_1+pcope_1
pcope~chla_1+pcope_1+hcope_1+clad_1+flow_1
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 33 iterations
##
## Optimization method NLMINB
## Number of free parameters 26
##
## Used Total
## Number of observations 233 311
##
## Estimator ML
## Model Fit Test Statistic 6.692
## Degrees of freedom 4
## P-value (Chi-square) 0.153
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## chla_1 0.235 0.063 3.735 0.000 0.235 0.238
## hcope_1 0.056 0.053 1.056 0.291 0.056 0.067
## amphi_1 0.106 0.055 1.941 0.052 0.106 0.142
## flow_1 0.081 0.066 1.234 0.217 0.081 0.090
## amphi ~
## chla_1 0.046 0.042 1.093 0.274 0.046 0.035
## amphi_1 0.753 0.037 20.406 0.000 0.753 0.762
## flow_1 -0.214 0.044 -4.873 0.000 -0.214 -0.181
## hcope ~
## chla_1 0.055 0.081 0.681 0.496 0.055 0.041
## hcope_1 0.455 0.069 6.562 0.000 0.455 0.395
## flow_1 -0.171 0.077 -2.233 0.026 -0.171 -0.139
## pcope_1 0.163 0.085 1.923 0.054 0.163 0.119
## pcope ~
## chla_1 -0.001 0.056 -0.025 0.980 -0.001 -0.001
## pcope_1 0.423 0.058 7.283 0.000 0.423 0.432
## hcope_1 -0.101 0.047 -2.140 0.032 -0.101 -0.123
## amphi_1 -0.114 0.049 -2.338 0.019 -0.114 -0.154
## flow_1 -0.033 0.059 -0.562 0.574 -0.033 -0.038
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla ~~
## .amphi 0.005 0.019 0.242 0.809 0.005 0.016
## .hcope 0.041 0.037 1.108 0.268 0.041 0.073
## .pcope 0.005 0.025 0.215 0.830 0.005 0.014
## .amphi ~~
## .hcope 0.008 0.025 0.311 0.756 0.008 0.020
## .pcope -0.023 0.017 -1.371 0.170 -0.023 -0.090
## .hcope ~~
## .pcope -0.045 0.033 -1.375 0.169 -0.045 -0.090
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.433 0.040 10.794 0.000 0.433 0.913
## .amphi 0.195 0.018 10.794 0.000 0.195 0.238
## .hcope 0.730 0.068 10.794 0.000 0.730 0.813
## .pcope 0.339 0.031 10.794 0.000 0.339 0.740
##
## R-Square:
## Estimate
## chla 0.087
## amphi 0.762
## hcope 0.187
## pcope 0.260summary(modfitW, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 30 iterations
##
## Optimization method NLMINB
## Number of free parameters 28
##
## Used Total
## Number of observations 304 311
##
## Estimator ML
## Model Fit Test Statistic 4.033
## Degrees of freedom 2
## P-value (Chi-square) 0.133
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## chla_1 0.289 0.056 5.139 0.000 0.289 0.290
## hcope_1 0.082 0.048 1.683 0.092 0.082 0.100
## flow_1 0.110 0.049 2.253 0.024 0.110 0.129
## hcope ~
## chla_1 0.092 0.069 1.336 0.182 0.092 0.075
## hcope_1 0.279 0.062 4.517 0.000 0.279 0.277
## mysid_1 0.182 0.062 2.914 0.004 0.182 0.168
## pcope_1 -0.068 0.064 -1.059 0.289 -0.068 -0.055
## flow_1 0.033 0.060 0.556 0.578 0.033 0.032
## pcope ~
## chla_1 -0.018 0.051 -0.346 0.729 -0.018 -0.018
## pcope_1 0.492 0.049 10.074 0.000 0.492 0.497
## hcope_1 -0.101 0.046 -2.190 0.029 -0.101 -0.125
## mysid_1 0.088 0.048 1.849 0.064 0.088 0.102
## flow_1 0.092 0.045 2.064 0.039 0.092 0.110
## mysid ~
## chla_1 0.151 0.058 2.610 0.009 0.151 0.134
## mysid_1 0.419 0.053 7.904 0.000 0.419 0.422
## hcope_1 0.110 0.052 2.119 0.034 0.110 0.119
## pcope_1 0.064 0.054 1.173 0.241 0.064 0.056
## flow_1 -0.007 0.050 -0.137 0.891 -0.007 -0.007
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla ~~
## .hcope 0.124 0.031 4.006 0.000 0.124 0.236
## .pcope -0.003 0.022 -0.150 0.881 -0.003 -0.009
## .mysid 0.071 0.026 2.779 0.005 0.071 0.161
## .hcope ~~
## .pcope 0.076 0.028 2.764 0.006 0.076 0.161
## .mysid 0.151 0.032 4.732 0.000 0.151 0.282
## .pcope ~~
## .mysid 0.025 0.023 1.106 0.269 0.025 0.064
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.435 0.035 12.329 0.000 0.435 0.878
## .hcope 0.640 0.052 12.329 0.000 0.640 0.844
## .pcope 0.354 0.029 12.329 0.000 0.354 0.724
## .mysid 0.449 0.036 12.329 0.000 0.449 0.702
##
## R-Square:
## Estimate
## chla 0.122
## hcope 0.156
## pcope 0.276
## mysid 0.298summary(modfitN, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 31 iterations
##
## Optimization method NLMINB
## Number of free parameters 25
##
## Used Total
## Number of observations 254 311
##
## Estimator ML
## Model Fit Test Statistic 4.306
## Degrees of freedom 5
## P-value (Chi-square) 0.506
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## chla_1 0.171 0.062 2.762 0.006 0.171 0.172
## hcope_1 -0.061 0.105 -0.580 0.562 -0.061 -0.043
## amphi_1 0.025 0.056 0.451 0.652 0.025 0.028
## flow_1 0.009 0.073 0.127 0.899 0.009 0.009
## amphi ~
## chla_1 0.081 0.059 1.371 0.170 0.081 0.074
## amphi_1 0.501 0.054 9.328 0.000 0.501 0.500
## flow_1 -0.020 0.060 -0.335 0.737 -0.020 -0.018
## hcope ~
## chla_1 0.003 0.037 0.078 0.938 0.003 0.004
## hcope_1 0.239 0.063 3.782 0.000 0.239 0.253
## flow_1 -0.180 0.044 -4.096 0.000 -0.180 -0.273
## pcope_1 -0.063 0.040 -1.581 0.114 -0.063 -0.090
## pcope ~
## chla_1 0.161 0.054 2.996 0.003 0.161 0.173
## pcope_1 0.371 0.058 6.443 0.000 0.371 0.372
## hcope_1 -0.154 0.091 -1.694 0.090 -0.154 -0.115
## flow_1 -0.059 0.064 -0.930 0.352 -0.059 -0.063
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla ~~
## .amphi -0.023 0.040 -0.580 0.562 -0.023 -0.036
## .hcope 0.032 0.025 1.282 0.200 0.032 0.081
## .pcope 0.007 0.036 0.199 0.842 0.007 0.013
## .amphi ~~
## .hcope 0.013 0.024 0.532 0.594 0.013 0.033
## .pcope -0.066 0.035 -1.904 0.057 -0.066 -0.120
## .hcope ~~
## .pcope 0.084 0.022 3.831 0.000 0.084 0.248
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.655 0.058 11.269 0.000 0.655 0.968
## .amphi 0.612 0.054 11.269 0.000 0.612 0.745
## .hcope 0.235 0.021 11.269 0.000 0.235 0.795
## .pcope 0.494 0.044 11.269 0.000 0.494 0.832
##
## R-Square:
## Estimate
## chla 0.032
## amphi 0.255
## hcope 0.205
## pcope 0.168summary(modfitS, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 35 iterations
##
## Optimization method NLMINB
## Number of free parameters 27
##
## Used Total
## Number of observations 304 312
##
## Estimator ML
## Model Fit Test Statistic 0.567
## Degrees of freedom 3
## P-value (Chi-square) 0.904
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## chla_1 0.328 0.055 5.912 0.000 0.328 0.329
## hcope_1 0.089 0.089 0.999 0.318 0.089 0.057
## clad_1 0.083 0.058 1.435 0.151 0.083 0.079
## flow_1 -0.167 0.058 -2.891 0.004 -0.167 -0.173
## clad ~
## chla_1 0.035 0.051 0.695 0.487 0.035 0.037
## clad_1 0.478 0.053 8.954 0.000 0.478 0.481
## flow_1 0.040 0.049 0.820 0.412 0.040 0.043
## pcope_1 -0.004 0.045 -0.094 0.925 -0.004 -0.005
## hcope ~
## chla_1 0.073 0.034 2.167 0.030 0.073 0.114
## hcope_1 0.334 0.057 5.872 0.000 0.334 0.332
## flow_1 -0.100 0.035 -2.887 0.004 -0.100 -0.161
## pcope_1 0.044 0.031 1.395 0.163 0.044 0.070
## pcope ~
## chla_1 0.115 0.056 2.047 0.041 0.115 0.113
## pcope_1 0.422 0.052 8.165 0.000 0.422 0.425
## hcope_1 -0.041 0.093 -0.445 0.656 -0.041 -0.026
## clad_1 0.113 0.060 1.889 0.059 0.113 0.105
## flow_1 0.042 0.059 0.709 0.478 0.042 0.042
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla ~~
## .clad 0.123 0.029 4.297 0.000 0.123 0.254
## .hcope 0.051 0.019 2.662 0.008 0.051 0.155
## .pcope -0.030 0.031 -0.973 0.331 -0.030 -0.056
## .clad ~~
## .hcope 0.051 0.018 2.852 0.004 0.051 0.166
## .pcope 0.050 0.028 1.765 0.078 0.050 0.102
## .hcope ~~
## .pcope 0.037 0.019 1.890 0.059 0.037 0.109
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.528 0.043 12.329 0.000 0.528 0.801
## .clad 0.446 0.036 12.329 0.000 0.446 0.750
## .hcope 0.210 0.017 12.329 0.000 0.210 0.765
## .pcope 0.539 0.044 12.329 0.000 0.539 0.776
##
## R-Square:
## Estimate
## chla 0.199
## clad 0.250
## hcope 0.235
## pcope 0.224#modificationindices(modfitW)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#FAR WEST
myLavaanPlot(model=modfitFW, labels=labelsnorth,
node_options=list(shape="box", fontname="Helvetica"),
coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
width=c("regress","latent"),
color=c("regress","latent"))#WEST
myLavaanPlot(model=modfitW, labels=labelsnorth,
node_options=list(shape="box", fontname="Helvetica"),
coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
width=c("regress","latent"),
color=c("regress","latent"))#NORTH
myLavaanPlot(model=modfitN, labels=labelsnorth,
node_options=list(shape="box", fontname="Helvetica"),
coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
width=c("regress","latent"),
color=c("regress","latent"))#SOUTH
myLavaanPlot(model=modfitS, labels=labelsnorth,
node_options=list(shape="box", fontname="Helvetica"),
coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
width=c("regress","latent"),
color=c("regress","latent"))# par(mfrow=c(2,2))
# semPaths(modfitFW, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("Far West", line = 1)
# semPaths(modfitW, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("West", line = 1)
# semPaths(modfitN, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("North", line = 1)
# semPaths(modfitS, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("South", line = 1)Zooplankton, fish
Also needs work. Note that because fish have more missing data, more rows get dropped (compared ‘used’ and ‘total’ number of observations to previous summaries).
If you get a warning about variances being negative, that is definitely a problem. It seems to happen when you try to create a latent variables, but the variables that go into it don’t acutally have a common trend.
modFW='fish=~estfish_bsmt+estfish_bsot
fish~hcope_1+amphi_1+mysid_1+flow_1
'
modWN='fish=~estfish_bsmt+estfish_bsot
fish~hcope_1+amphi_1+clad_1+mysid_1+flow_1
'
modS='#fish=~estfish_bsmt+estfish_bsot
estfish_bsmt~hcope_1+amphi_1+clad_1+mysid_1+flow_1
estfish_bsot~hcope_1+amphi_1+clad_1+mysid_1+flow_1
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modWN, data=filter(fdr_ds,region=="West"))
modfitN=sem(modWN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 21 iterations
##
## Optimization method NLMINB
## Number of free parameters 8
##
## Used Total
## Number of observations 125 311
##
## Estimator ML
## Model Fit Test Statistic 8.555
## Degrees of freedom 3
## P-value (Chi-square) 0.036
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish_bsmt 1.000 0.507 0.556
## estfish_bsot 1.149 0.304 3.774 0.000 0.583 0.607
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish ~
## hcope_1 -0.168 0.059 -2.823 0.005 -0.330 -0.361
## amphi_1 -0.130 0.074 -1.753 0.080 -0.257 -0.269
## mysid_1 -0.104 0.079 -1.312 0.189 -0.204 -0.167
## flow_1 0.244 0.109 2.233 0.026 0.480 0.326
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_bsmt 0.575 0.099 5.784 0.000 0.575 0.691
## .estfish_bsot 0.581 0.116 5.022 0.000 0.581 0.631
## .fish 0.142 0.070 2.024 0.043 0.551 0.551
##
## R-Square:
## Estimate
## estfish_bsmt 0.309
## estfish_bsot 0.369
## fish 0.449summary(modfitW, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 22 iterations
##
## Optimization method NLMINB
## Number of free parameters 9
##
## Used Total
## Number of observations 132 311
##
## Estimator ML
## Model Fit Test Statistic 15.518
## Degrees of freedom 4
## P-value (Chi-square) 0.004
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish_bsmt 1.000 0.519 0.561
## estfish_bsot 0.356 0.200 1.777 0.076 0.185 0.226
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish ~
## hcope_1 0.231 0.101 2.290 0.022 0.445 0.365
## amphi_1 -0.283 0.074 -3.829 0.000 -0.545 -0.543
## clad_1 0.116 0.110 1.055 0.291 0.223 0.183
## mysid_1 -0.069 0.108 -0.645 0.519 -0.134 -0.103
## flow_1 -0.140 0.133 -1.048 0.295 -0.269 -0.179
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_bsmt 0.588 0.198 2.973 0.003 0.588 0.686
## .estfish_bsot 0.633 0.081 7.783 0.000 0.633 0.949
## .fish 0.136 0.185 0.736 0.462 0.505 0.505
##
## R-Square:
## Estimate
## estfish_bsmt 0.314
## estfish_bsot 0.051
## fish 0.495summary(modfitN, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 28 iterations
##
## Optimization method NLMINB
## Number of free parameters 9
##
## Used Total
## Number of observations 126 311
##
## Estimator ML
## Model Fit Test Statistic 14.245
## Degrees of freedom 4
## P-value (Chi-square) 0.007
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish_bsmt 1.000 0.344 0.513
## estfish_bsot 1.614 0.321 5.030 0.000 0.555 0.675
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish ~
## hcope_1 0.143 0.085 1.668 0.095 0.415 0.218
## amphi_1 0.052 0.036 1.440 0.150 0.150 0.136
## clad_1 0.065 0.046 1.426 0.154 0.191 0.149
## mysid_1 -0.048 0.052 -0.926 0.354 -0.140 -0.110
## flow_1 -0.479 0.093 -5.125 0.000 -1.392 -0.839
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_bsmt 0.332 0.047 7.030 0.000 0.332 0.737
## .estfish_bsot 0.367 0.073 5.001 0.000 0.367 0.544
## .fish 0.023 0.022 1.051 0.293 0.197 0.197
##
## R-Square:
## Estimate
## estfish_bsmt 0.263
## estfish_bsot 0.456
## fish 0.803summary(modfitS, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 20 iterations
##
## Optimization method NLMINB
## Number of free parameters 13
##
## Used Total
## Number of observations 129 312
##
## Estimator ML
## Model Fit Test Statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## estfish_bsmt ~
## hcope_1 -0.044 0.132 -0.336 0.737 -0.044 -0.032
## amphi_1 0.113 0.068 1.679 0.093 0.113 0.151
## clad_1 0.021 0.072 0.296 0.768 0.021 0.026
## mysid_1 0.160 0.093 1.732 0.083 0.160 0.168
## flow_1 -0.019 0.082 -0.235 0.814 -0.019 -0.024
## estfish_bsot ~
## hcope_1 -0.189 0.176 -1.074 0.283 -0.189 -0.105
## amphi_1 -0.059 0.090 -0.653 0.514 -0.059 -0.060
## clad_1 -0.164 0.095 -1.716 0.086 -0.164 -0.154
## mysid_1 0.099 0.123 0.800 0.424 0.099 0.078
## flow_1 -0.013 0.110 -0.118 0.906 -0.013 -0.012
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_bsmt ~~
## .estfish_bsot -0.017 0.049 -0.338 0.735 -0.017 -0.030
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_bsmt 0.421 0.052 8.031 0.000 0.421 0.937
## .estfish_bsot 0.747 0.093 8.031 0.000 0.747 0.962
##
## R-Square:
## Estimate
## estfish_bsmt 0.063
## estfish_bsot 0.038#par(mfrow=c(1,1))
par(mfrow=c(2,2))
semPaths(modfitFW, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("Far West", line = 1)
semPaths(modfitW, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("West", line = 1)
semPaths(modfitN, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("North", line = 1)
semPaths(modfitS, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("South", line = 1)
Integrated model
South
modS='ammonia~flow_1+chla_1+clad_1
dophos~flow_1+chla_1+clad_1
ammonia~~dophos+clad
chla~chla_1+hcope_1+clad_1+ammonia+dophos+flow_1+corbic_1
hcope~chla_1+hcope_1+flow_1+pcope_1
pcope~pcope_1+hcope_1+clad_1
clad~chla_1+clad_1+flow_1
corbic~corbic_1+flow_1
'
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitS, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 36 iterations
##
## Optimization method NLMINB
## Number of free parameters 44
##
## Used Total
## Number of observations 255 312
##
## Estimator ML
## Model Fit Test Statistic 47.782
## Degrees of freedom 26
## P-value (Chi-square) 0.006
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ammonia ~
## flow_1 0.016 0.055 0.298 0.766 0.016 0.019
## chla_1 0.200 0.060 3.330 0.001 0.200 0.218
## clad_1 -0.168 0.061 -2.744 0.006 -0.168 -0.181
## dophos ~
## flow_1 0.104 0.072 1.455 0.146 0.104 0.094
## chla_1 -0.002 0.078 -0.026 0.980 -0.002 -0.002
## clad_1 0.188 0.079 2.377 0.017 0.188 0.158
## chla ~
## chla_1 0.328 0.059 5.579 0.000 0.328 0.327
## hcope_1 0.097 0.093 1.041 0.298 0.097 0.062
## clad_1 0.089 0.060 1.473 0.141 0.089 0.087
## ammonia -0.287 0.060 -4.748 0.000 -0.287 -0.263
## dophos -0.128 0.046 -2.813 0.005 -0.128 -0.151
## flow_1 -0.167 0.059 -2.845 0.004 -0.167 -0.177
## corbic_1 0.062 0.042 1.484 0.138 0.062 0.077
## hcope ~
## chla_1 0.052 0.035 1.472 0.141 0.052 0.084
## hcope_1 0.326 0.060 5.417 0.000 0.326 0.335
## flow_1 -0.101 0.036 -2.837 0.005 -0.101 -0.173
## pcope_1 0.035 0.032 1.072 0.284 0.035 0.059
## pcope ~
## pcope_1 0.458 0.055 8.368 0.000 0.458 0.463
## hcope_1 -0.133 0.088 -1.502 0.133 -0.133 -0.081
## clad_1 0.126 0.059 2.126 0.033 0.126 0.119
## clad ~
## chla_1 0.058 0.054 1.069 0.285 0.058 0.059
## clad_1 0.517 0.055 9.368 0.000 0.517 0.524
## flow_1 0.030 0.050 0.600 0.548 0.030 0.033
## corbic ~
## corbic_1 0.344 0.056 6.141 0.000 0.344 0.357
## flow_1 0.110 0.065 1.686 0.092 0.110 0.098
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ammonia ~~
## .dophos 0.149 0.042 3.582 0.000 0.149 0.225
## .clad -0.092 0.028 -3.226 0.001 -0.092 -0.197
## .chla ~~
## .hcope 0.043 0.019 2.281 0.023 0.043 0.144
## .pcope -0.019 0.031 -0.607 0.544 -0.019 -0.038
## .clad 0.103 0.028 3.677 0.000 0.103 0.232
## .corbic -0.014 0.037 -0.386 0.700 -0.014 -0.024
## .hcope ~~
## .pcope 0.031 0.020 1.549 0.121 0.031 0.097
## .clad 0.039 0.018 2.202 0.028 0.039 0.136
## .corbic 0.021 0.024 0.851 0.395 0.021 0.053
## .pcope ~~
## .clad 0.072 0.029 2.474 0.013 0.072 0.154
## .corbic 0.008 0.040 0.212 0.832 0.008 0.013
## .clad ~~
## .corbic -0.023 0.035 -0.656 0.512 -0.023 -0.040
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ammonia 0.512 0.045 11.315 0.000 0.512 0.945
## .dophos 0.857 0.076 11.292 0.000 0.857 0.961
## .chla 0.463 0.041 11.292 0.000 0.463 0.719
## .hcope 0.191 0.017 11.292 0.000 0.191 0.777
## .pcope 0.520 0.046 11.292 0.000 0.520 0.747
## .clad 0.425 0.038 11.310 0.000 0.425 0.697
## .corbic 0.772 0.068 11.292 0.000 0.772 0.854
##
## R-Square:
## Estimate
## ammonia 0.055
## dophos 0.039
## chla 0.281
## hcope 0.223
## pcope 0.253
## clad 0.303
## corbic 0.146# semPaths(modfitS, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("South", line = 1)
residuals(modfitS)## $type
## [1] "raw"
##
## $cov
## ammoni dophos chla hcope pcope clad corbic flow_1 chla_1
## ammonia 0.000
## dophos 0.000 0.000
## chla 0.004 -0.003 0.000
## hcope 0.020 0.044 -0.005 0.001
## pcope 0.046 0.180 -0.018 0.003 -0.001
## clad 0.006 0.025 0.000 0.003 0.004 0.002
## corbic 0.052 -0.142 0.018 0.019 -0.043 -0.003 0.004
## flow_1 0.000 0.000 0.000 0.000 0.003 0.000 0.000 0.000
## chla_1 0.000 0.000 -0.003 0.005 0.070 0.009 0.024 0.000 0.000
## clad_1 0.000 0.000 0.001 0.003 0.001 0.001 0.001 0.000 0.000
## hcope_1 0.020 0.015 -0.005 0.001 0.002 0.006 -0.005 0.000 0.000
## corbic_1 0.028 -0.142 0.032 0.065 -0.040 0.014 0.005 0.000 0.000
## pcope_1 0.020 0.132 -0.026 -0.002 -0.001 0.002 -0.067 0.000 0.000
## clad_1 hcop_1 crbc_1 pcop_1
## ammonia
## dophos
## chla
## hcope
## pcope
## clad
## corbic
## flow_1
## chla_1
## clad_1 0.000
## hcope_1 0.000 0.000
## corbic_1 0.000 0.000 0.000
## pcope_1 0.000 0.000 0.000 0.000modificationindices(modfitS)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 45 flow_1 ~~ flow_1 0.000 0.000 0.000 0.000 0.000
## 46 flow_1 ~~ chla_1 0.000 0.000 0.000 NA 0.000
## 47 flow_1 ~~ clad_1 0.000 0.000 0.000 NA 0.000
## 48 flow_1 ~~ hcope_1 0.000 0.000 0.000 NA 0.000
## 49 flow_1 ~~ corbic_1 0.000 0.000 0.000 NA 0.000
## 50 flow_1 ~~ pcope_1 0.000 0.000 0.000 NA 0.000
## 51 chla_1 ~~ chla_1 0.000 0.000 0.000 0.000 0.000
## 52 chla_1 ~~ clad_1 0.000 0.000 0.000 NA 0.000
## 53 chla_1 ~~ hcope_1 0.000 0.000 0.000 NA 0.000
## 54 chla_1 ~~ corbic_1 0.000 0.000 0.000 NA 0.000
## 55 chla_1 ~~ pcope_1 0.000 0.000 0.000 NA 0.000
## 56 clad_1 ~~ clad_1 0.000 0.000 0.000 0.000 0.000
## 57 clad_1 ~~ hcope_1 0.000 0.000 0.000 NA 0.000
## 58 clad_1 ~~ corbic_1 0.000 0.000 0.000 NA 0.000
## 59 clad_1 ~~ pcope_1 0.000 0.000 0.000 NA 0.000
## 60 hcope_1 ~~ hcope_1 0.000 0.000 0.000 0.000 0.000
## 61 hcope_1 ~~ corbic_1 0.000 0.000 0.000 NA 0.000
## 62 hcope_1 ~~ pcope_1 0.000 0.000 0.000 NA 0.000
## 63 corbic_1 ~~ corbic_1 0.000 0.000 0.000 0.000 0.000
## 64 corbic_1 ~~ pcope_1 0.000 0.000 0.000 NA 0.000
## 65 pcope_1 ~~ pcope_1 0.000 0.000 0.000 0.000 0.000
## 67 ammonia ~~ hcope 0.073 0.005 0.005 0.016 0.016
## 68 ammonia ~~ pcope 0.009 0.003 0.003 0.006 0.006
## 69 ammonia ~~ corbic 1.939 0.053 0.053 0.085 0.085
## 71 dophos ~~ hcope 1.136 0.026 0.026 0.064 0.064
## 72 dophos ~~ pcope 7.162 0.107 0.107 0.161 0.161
## 73 dophos ~~ clad 0.000 -0.001 -0.001 -0.001 -0.001
## 74 dophos ~~ corbic 4.816 -0.109 -0.109 -0.134 -0.134
## 76 ammonia ~ chla 0.900 0.309 0.309 0.337 0.337
## 77 ammonia ~ hcope 0.419 0.061 0.061 0.041 0.041
## 78 ammonia ~ pcope 0.000 0.001 0.001 0.001 0.001
## 79 ammonia ~ clad 0.001 -0.008 -0.008 -0.008 -0.008
## 80 ammonia ~ corbic 3.210 0.083 0.083 0.107 0.107
## 81 ammonia ~ hcope_1 0.970 0.094 0.094 0.065 0.128
## 82 ammonia ~ corbic_1 1.720 0.057 0.057 0.077 0.078
## 83 ammonia ~ pcope_1 0.005 -0.004 -0.004 -0.004 -0.005
## 84 dophos ~ ammonia 0.000 0.008 0.008 0.006 0.006
## 85 dophos ~ chla 0.139 -0.091 -0.091 -0.078 -0.078
## 86 dophos ~ hcope 2.044 0.175 0.175 0.092 0.092
## 87 dophos ~ pcope 13.860 0.260 0.260 0.229 0.229
## 88 dophos ~ clad 0.421 0.058 0.058 0.048 0.048
## 89 dophos ~ corbic 8.690 -0.177 -0.177 -0.178 -0.178
## 90 dophos ~ hcope_1 0.110 0.042 0.042 0.023 0.044
## 91 dophos ~ corbic_1 7.505 -0.158 -0.158 -0.165 -0.167
## 92 dophos ~ pcope_1 7.674 0.194 0.194 0.173 0.205
## 93 chla ~ hcope 0.018 -0.200 -0.200 -0.124 -0.124
## 94 chla ~ pcope 0.018 -0.015 -0.015 -0.016 -0.016
## 97 chla ~ pcope_1 0.018 -0.007 -0.007 -0.007 -0.009
## 98 hcope ~ ammonia 0.241 0.018 0.018 0.027 0.027
## 99 hcope ~ dophos 1.366 0.034 0.034 0.064 0.064
## 100 hcope ~ chla 0.089 -0.029 -0.029 -0.047 -0.047
## 101 hcope ~ pcope 0.030 0.055 0.055 0.092 0.092
## 102 hcope ~ clad 0.005 0.005 0.005 0.008 0.008
## 103 hcope ~ corbic 5.977 0.198 0.198 0.379 0.379
## 104 hcope ~ clad_1 0.030 0.007 0.007 0.011 0.014
## 105 hcope ~ corbic_1 5.977 0.068 0.068 0.135 0.137
## 106 pcope ~ ammonia 1.274 0.069 0.069 0.061 0.061
## 107 pcope ~ dophos 7.626 0.132 0.132 0.150 0.150
## 108 pcope ~ chla 0.166 -0.050 -0.050 -0.048 -0.048
## 109 pcope ~ hcope 0.669 0.412 0.412 0.245 0.245
## 110 pcope ~ clad 0.318 0.182 0.182 0.170 0.170
## 111 pcope ~ corbic 1.200 -0.135 -0.135 -0.154 -0.154
## 112 pcope ~ flow_1 0.008 0.005 0.005 0.006 0.006
## 113 pcope ~ chla_1 4.332 0.127 0.127 0.122 0.152
## 114 pcope ~ corbic_1 1.410 -0.054 -0.054 -0.063 -0.064
## 115 clad ~ ammonia 0.000 -0.005 -0.005 -0.005 -0.005
## 116 clad ~ dophos 0.000 -0.001 -0.001 -0.001 -0.001
## 117 clad ~ chla 0.086 0.064 0.064 0.066 0.066
## 118 clad ~ hcope 0.230 0.126 0.126 0.080 0.080
## 119 clad ~ pcope 0.006 -0.008 -0.008 -0.009 -0.009
## 120 clad ~ corbic 0.214 0.054 0.054 0.065 0.065
## 121 clad ~ hcope_1 0.247 0.044 0.044 0.029 0.057
## 122 clad ~ corbic_1 0.214 0.018 0.018 0.023 0.024
## 123 clad ~ pcope_1 0.000 0.000 0.000 0.000 0.000
## 124 corbic ~ ammonia 1.017 0.075 0.075 0.058 0.058
## 125 corbic ~ dophos 3.633 -0.112 -0.112 -0.111 -0.111
## 126 corbic ~ chla 0.155 0.058 0.058 0.049 0.049
## 127 corbic ~ hcope 0.080 -0.093 -0.093 -0.048 -0.048
## 128 corbic ~ pcope 1.751 -0.175 -0.175 -0.154 -0.154
## 129 corbic ~ clad 0.123 -0.044 -0.044 -0.036 -0.036
## 130 corbic ~ chla_1 0.284 0.037 0.037 0.031 0.039
## 131 corbic ~ clad_1 0.000 0.001 0.001 0.001 0.001
## 132 corbic ~ hcope_1 0.032 -0.022 -0.022 -0.012 -0.023
## 133 corbic ~ pcope_1 2.122 -0.095 -0.095 -0.084 -0.100
## 134 flow_1 ~ ammonia 0.512 0.093 0.093 0.081 0.081
## 135 flow_1 ~ dophos 2.043 0.146 0.146 0.162 0.162
## 136 flow_1 ~ chla 1.038 -0.247 -0.247 -0.233 -0.233
## 137 flow_1 ~ hcope 0.679 -0.189 -0.189 -0.110 -0.110
## 138 flow_1 ~ pcope 0.199 0.021 0.021 0.020 0.020
## 139 flow_1 ~ clad 0.035 0.018 0.018 0.016 0.016
## 140 flow_1 ~ corbic 0.002 -0.004 -0.004 -0.004 -0.004
## 141 flow_1 ~ chla_1 0.000 0.000 0.000 0.000 0.000
## 142 flow_1 ~ clad_1 0.000 0.000 0.000 0.000 0.000
## 143 flow_1 ~ hcope_1 0.000 0.000 0.000 0.000 0.000
## 144 flow_1 ~ corbic_1 0.000 0.000 0.000 0.000 0.000
## 145 flow_1 ~ pcope_1 0.000 0.000 0.000 0.000 0.000
## 146 chla_1 ~ ammonia 0.057 -0.071 -0.071 -0.065 -0.065
## 147 chla_1 ~ dophos 2.697 0.382 0.382 0.451 0.451
## 148 chla_1 ~ chla 0.567 -0.212 -0.212 -0.213 -0.213
## 149 chla_1 ~ hcope 0.126 0.083 0.083 0.051 0.051
## 150 chla_1 ~ pcope 2.424 0.073 0.073 0.076 0.076
## 151 chla_1 ~ clad 0.377 0.102 0.102 0.099 0.099
## 152 chla_1 ~ corbic 0.112 0.015 0.015 0.018 0.018
## 153 chla_1 ~ flow_1 0.000 0.000 0.000 0.000 0.000
## 154 chla_1 ~ clad_1 0.000 0.000 0.000 0.000 0.000
## 155 chla_1 ~ hcope_1 0.000 0.000 0.000 0.000 0.000
## 156 chla_1 ~ corbic_1 0.000 0.000 0.000 0.000 0.000
## 157 chla_1 ~ pcope_1 0.000 0.000 0.000 0.000 0.000
## 158 clad_1 ~ ammonia 0.666 -0.145 -0.145 -0.135 -0.135
## 159 clad_1 ~ dophos 7.196 -0.374 -0.374 -0.448 -0.448
## 160 clad_1 ~ chla 0.414 0.083 0.083 0.084 0.084
## 161 clad_1 ~ hcope 0.016 0.010 0.010 0.006 0.006
## 162 clad_1 ~ pcope 1.438 -0.118 -0.118 -0.125 -0.125
## 163 clad_1 ~ clad 0.063 -0.033 -0.033 -0.033 -0.033
## 164 clad_1 ~ corbic 0.051 0.009 0.009 0.011 0.011
## 165 clad_1 ~ flow_1 0.000 0.000 0.000 0.000 0.000
## 166 clad_1 ~ chla_1 0.000 0.000 0.000 0.000 0.000
## 167 clad_1 ~ hcope_1 0.000 0.000 0.000 0.000 0.000
## 168 clad_1 ~ corbic_1 0.000 0.000 0.000 0.000 0.000
## 169 clad_1 ~ pcope_1 0.000 0.000 0.000 0.000 0.000
## 170 hcope_1 ~ ammonia 0.696 0.030 0.030 0.044 0.044
## 171 hcope_1 ~ dophos 0.350 0.017 0.017 0.031 0.031
## 172 hcope_1 ~ chla 0.319 -0.044 -0.044 -0.068 -0.068
## 173 hcope_1 ~ hcope 0.866 -0.195 -0.195 -0.190 -0.190
## 174 hcope_1 ~ pcope 0.007 -0.004 -0.004 -0.007 -0.007
## 175 hcope_1 ~ clad 0.025 0.005 0.005 0.007 0.007
## 176 hcope_1 ~ corbic 0.018 -0.004 -0.004 -0.007 -0.007
## 177 hcope_1 ~ flow_1 0.000 0.000 0.000 0.000 0.000
## 178 hcope_1 ~ chla_1 0.000 0.000 0.000 0.000 0.000
## 179 hcope_1 ~ clad_1 0.000 0.000 0.000 0.000 0.000
## 180 hcope_1 ~ corbic_1 0.000 0.000 0.000 0.000 0.000
## 181 hcope_1 ~ pcope_1 0.000 0.000 0.000 0.000 0.000
## 182 corbic_1 ~ ammonia 0.247 0.040 0.040 0.030 0.030
## 183 corbic_1 ~ dophos 5.964 -0.153 -0.153 -0.146 -0.146
## 184 corbic_1 ~ chla 1.622 0.184 0.184 0.150 0.150
## 185 corbic_1 ~ hcope 3.474 0.200 0.200 0.101 0.101
## 186 corbic_1 ~ pcope 0.461 -0.042 -0.042 -0.035 -0.035
## 187 corbic_1 ~ clad 0.048 0.014 0.014 0.011 0.011
## 188 corbic_1 ~ corbic 0.505 0.324 0.324 0.312 0.312
## 189 corbic_1 ~ flow_1 0.000 0.000 0.000 0.000 0.000
## 190 corbic_1 ~ chla_1 0.000 0.000 0.000 0.000 0.000
## 191 corbic_1 ~ clad_1 0.000 0.000 0.000 0.000 0.000
## 192 corbic_1 ~ hcope_1 0.000 0.000 0.000 0.000 0.000
## 193 corbic_1 ~ pcope_1 0.000 0.000 0.000 0.000 0.000
## 194 pcope_1 ~ ammonia 0.199 0.029 0.029 0.026 0.026
## 195 pcope_1 ~ dophos 6.920 0.136 0.136 0.152 0.152
## 196 pcope_1 ~ chla 0.324 -0.032 -0.032 -0.030 -0.030
## 197 pcope_1 ~ hcope 0.035 -0.042 -0.042 -0.025 -0.025
## 198 pcope_1 ~ pcope 0.510 0.124 0.124 0.123 0.123
## 199 pcope_1 ~ clad 0.002 0.002 0.002 0.002 0.002
## 200 pcope_1 ~ corbic 1.426 -0.057 -0.057 -0.065 -0.065
## 201 pcope_1 ~ flow_1 0.000 0.000 0.000 0.000 0.000
## 202 pcope_1 ~ chla_1 0.000 0.000 0.000 0.000 0.000
## 203 pcope_1 ~ clad_1 0.000 0.000 0.000 0.000 0.000
## 204 pcope_1 ~ hcope_1 0.000 0.000 0.000 0.000 0.000
## 205 pcope_1 ~ corbic_1 0.000 0.000 0.000 0.000 0.000labelssouth=createLabels(modfitS, cnameslag)
myLavaanPlot(model=modfitS, labels=labelssouth,
node_options=list(shape="box", fontname="Helvetica"),
coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
width=c("regress","latent"),
color=c("regress","latent"))North
modN='ammonia~flow_1+chla_1+clad_1
dophos~flow_1+chla_1+clad_1
ammonia~~dophos+clad
dophos~~clad
chla~chla_1+hcope_1+clad_1+ammonia+dophos+flow_1+corbic_1
hcope~chla_1+hcope_1+flow_1
clad~chla_1+clad_1+flow_1
corbic~corbic_1+flow_1
'
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
summary(modfitN, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 33 iterations
##
## Optimization method NLMINB
## Number of free parameters 36
##
## Used Total
## Number of observations 254 311
##
## Estimator ML
## Model Fit Test Statistic 21.390
## Degrees of freedom 15
## P-value (Chi-square) 0.125
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ammonia ~
## flow_1 -0.232 0.055 -4.181 0.000 -0.232 -0.263
## chla_1 0.042 0.053 0.804 0.421 0.042 0.048
## clad_1 -0.084 0.060 -1.392 0.164 -0.084 -0.088
## dophos ~
## flow_1 -0.294 0.063 -4.677 0.000 -0.294 -0.269
## chla_1 -0.050 0.060 -0.842 0.400 -0.050 -0.046
## clad_1 -0.382 0.068 -5.615 0.000 -0.382 -0.324
## chla ~
## chla_1 0.163 0.060 2.733 0.006 0.163 0.163
## hcope_1 -0.029 0.101 -0.285 0.776 -0.029 -0.020
## clad_1 0.090 0.071 1.262 0.207 0.090 0.083
## ammonia -0.309 0.070 -4.399 0.000 -0.309 -0.272
## dophos -0.060 0.062 -0.971 0.331 -0.060 -0.065
## flow_1 -0.106 0.074 -1.425 0.154 -0.106 -0.105
## corbic_1 -0.067 0.050 -1.341 0.180 -0.067 -0.079
## hcope ~
## chla_1 0.005 0.037 0.133 0.894 0.005 0.008
## hcope_1 0.202 0.062 3.249 0.001 0.202 0.215
## flow_1 -0.185 0.044 -4.219 0.000 -0.185 -0.282
## clad ~
## chla_1 -0.071 0.050 -1.412 0.158 -0.071 -0.077
## clad_1 0.431 0.056 7.642 0.000 0.431 0.431
## flow_1 0.153 0.053 2.907 0.004 0.153 0.165
## corbic ~
## corbic_1 0.494 0.054 9.061 0.000 0.494 0.493
## flow_1 0.059 0.064 0.919 0.358 0.059 0.050
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ammonia ~~
## .dophos 0.097 0.035 2.795 0.005 0.097 0.178
## .clad -0.022 0.028 -0.783 0.433 -0.022 -0.049
## .dophos ~~
## .clad -0.125 0.033 -3.787 0.000 -0.125 -0.242
## .chla ~~
## .hcope 0.044 0.024 1.851 0.064 0.044 0.117
## .clad 0.020 0.031 0.634 0.526 0.020 0.039
## .corbic -0.072 0.041 -1.740 0.082 -0.072 -0.110
## .hcope ~~
## .clad -0.050 0.020 -2.534 0.011 -0.050 -0.156
## .corbic -0.017 0.026 -0.662 0.508 -0.017 -0.042
## .clad ~~
## .corbic 0.002 0.034 0.074 0.941 0.002 0.004
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ammonia 0.480 0.043 11.269 0.000 0.480 0.909
## .dophos 0.616 0.055 11.269 0.000 0.616 0.762
## .chla 0.596 0.053 11.269 0.000 0.596 0.874
## .hcope 0.237 0.021 11.269 0.000 0.237 0.810
## .clad 0.434 0.038 11.286 0.000 0.434 0.745
## .corbic 0.714 0.063 11.269 0.000 0.714 0.754
##
## R-Square:
## Estimate
## ammonia 0.091
## dophos 0.238
## chla 0.126
## hcope 0.190
## clad 0.255
## corbic 0.246# semPaths(modfitS, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("South", line = 1)
residuals(modfitN)## $type
## [1] "raw"
##
## $cov
## ammoni dophos chla hcope clad corbic flow_1 chla_1 clad_1
## ammonia 0.000
## dophos 0.000 0.000
## chla 0.001 0.020 -0.006
## hcope 0.025 0.050 -0.012 0.002
## clad -0.005 -0.011 -0.003 -0.022 0.006
## corbic 0.023 -0.085 0.010 0.000 0.039 -0.001
## flow_1 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## chla_1 0.000 0.000 -0.003 -0.001 0.000 0.029 0.000 0.000
## clad_1 0.000 0.000 -0.007 -0.007 0.001 0.061 0.000 0.000 0.000
## hcope_1 0.004 -0.016 0.000 0.006 -0.043 -0.021 0.000 0.000 0.000
## corbic_1 0.046 -0.090 -0.007 0.008 0.024 -0.001 0.000 0.000 0.000
## hcop_1 crbc_1
## ammonia
## dophos
## chla
## hcope
## clad
## corbic
## flow_1
## chla_1
## clad_1
## hcope_1 0.000
## corbic_1 0.000 0.000modificationindices(modfitN)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 37 flow_1 ~~ flow_1 0.000 0.000 0.000 0.000 0.000
## 38 flow_1 ~~ chla_1 0.000 0.000 0.000 NA 0.000
## 39 flow_1 ~~ clad_1 0.000 0.000 0.000 NA 0.000
## 40 flow_1 ~~ hcope_1 0.000 0.000 0.000 NA 0.000
## 41 flow_1 ~~ corbic_1 0.000 0.000 0.000 NA 0.000
## 42 chla_1 ~~ chla_1 0.000 0.000 0.000 0.000 0.000
## 43 chla_1 ~~ clad_1 0.000 0.000 0.000 NA 0.000
## 44 chla_1 ~~ hcope_1 0.000 0.000 0.000 NA 0.000
## 45 chla_1 ~~ corbic_1 0.000 0.000 0.000 NA 0.000
## 46 clad_1 ~~ clad_1 0.000 0.000 0.000 0.000 0.000
## 47 clad_1 ~~ hcope_1 0.000 0.000 0.000 NA 0.000
## 48 clad_1 ~~ corbic_1 0.000 0.000 0.000 NA 0.000
## 49 hcope_1 ~~ hcope_1 0.000 0.000 0.000 0.000 0.000
## 50 hcope_1 ~~ corbic_1 0.000 0.000 0.000 NA 0.000
## 51 corbic_1 ~~ corbic_1 0.000 0.000 0.000 0.000 0.000
## 53 ammonia ~~ hcope 0.568 0.016 0.016 0.047 0.047
## 54 ammonia ~~ corbic 0.044 0.008 0.008 0.013 0.013
## 56 dophos ~~ hcope 3.749 0.046 0.046 0.119 0.119
## 57 dophos ~~ corbic 0.102 -0.013 -0.013 -0.020 -0.020
## 59 ammonia ~ chla 0.126 -0.110 -0.110 -0.125 -0.125
## 60 ammonia ~ hcope 0.629 0.068 0.068 0.051 0.051
## 61 ammonia ~ clad 0.560 -0.311 -0.311 -0.327 -0.327
## 62 ammonia ~ corbic 0.769 0.039 0.039 0.052 0.052
## 63 ammonia ~ hcope_1 0.099 0.028 0.028 0.022 0.039
## 64 ammonia ~ corbic_1 2.172 0.066 0.066 0.088 0.090
## 66 dophos ~ chla 6.531 0.892 0.892 0.819 0.819
## 67 dophos ~ hcope 2.744 0.161 0.161 0.097 0.097
## 68 dophos ~ clad 3.791 -0.917 -0.917 -0.778 -0.778
## 69 dophos ~ corbic 1.857 -0.068 -0.068 -0.073 -0.073
## 70 dophos ~ hcope_1 1.640 -0.127 -0.127 -0.081 -0.141
## 71 dophos ~ corbic_1 4.120 -0.099 -0.099 -0.107 -0.111
## 75 hcope ~ ammonia 1.289 0.050 0.050 0.067 0.067
## 76 hcope ~ dophos 4.309 0.076 0.076 0.127 0.127
## 77 hcope ~ chla 2.285 -0.182 -0.182 -0.278 -0.278
## 78 hcope ~ clad 1.474 -0.107 -0.107 -0.151 -0.151
## 79 hcope ~ corbic 0.084 0.018 0.018 0.033 0.033
## 80 hcope ~ clad_1 0.072 -0.011 -0.011 -0.016 -0.021
## 81 hcope ~ corbic_1 0.084 0.009 0.009 0.016 0.017
## 84 clad ~ chla 0.086 0.177 0.177 0.192 0.192
## 85 clad ~ hcope 5.875 -1.008 -1.008 -0.716 -0.716
## 86 clad ~ corbic 0.045 0.018 0.018 0.022 0.022
## 87 clad ~ hcope_1 5.875 -0.204 -0.204 -0.154 -0.267
## 88 clad ~ corbic_1 0.045 0.009 0.009 0.011 0.011
## 89 corbic ~ ammonia 0.003 0.004 0.004 0.003 0.003
## 90 corbic ~ dophos 0.679 -0.052 -0.052 -0.048 -0.048
## 91 corbic ~ chla 0.676 0.154 0.154 0.131 0.131
## 92 corbic ~ hcope 0.599 -0.413 -0.413 -0.230 -0.230
## 93 corbic ~ clad 2.025 0.215 0.215 0.169 0.169
## 94 corbic ~ chla_1 0.435 0.042 0.042 0.036 0.044
## 95 corbic ~ clad_1 2.482 0.115 0.115 0.090 0.119
## 96 corbic ~ hcope_1 0.649 -0.088 -0.088 -0.052 -0.090
## 97 flow_1 ~ ammonia 0.052 0.025 0.025 0.022 0.022
## 98 flow_1 ~ dophos 0.314 -0.045 -0.045 -0.050 -0.050
## 99 flow_1 ~ chla 0.021 0.041 0.041 0.041 0.041
## 100 flow_1 ~ hcope 0.919 0.294 0.294 0.193 0.193
## 101 flow_1 ~ clad 2.438 -0.148 -0.148 -0.137 -0.137
## 102 flow_1 ~ corbic 0.914 -0.062 -0.062 -0.073 -0.073
## 103 flow_1 ~ chla_1 0.000 0.000 0.000 0.000 0.000
## 104 flow_1 ~ clad_1 0.000 0.000 0.000 0.000 0.000
## 105 flow_1 ~ hcope_1 0.000 0.000 0.000 0.000 0.000
## 106 flow_1 ~ corbic_1 0.000 0.000 0.000 0.000 0.000
## 107 chla_1 ~ ammonia 0.232 0.168 0.168 0.147 0.147
## 108 chla_1 ~ dophos 0.254 -0.128 -0.128 -0.139 -0.139
## 109 chla_1 ~ chla 0.132 -0.139 -0.139 -0.138 -0.138
## 110 chla_1 ~ hcope 0.006 -0.036 -0.036 -0.024 -0.024
## 111 chla_1 ~ clad 2.073 0.421 0.421 0.387 0.387
## 112 chla_1 ~ corbic 0.197 0.020 0.020 0.024 0.024
## 113 chla_1 ~ flow_1 0.000 0.000 0.000 0.000 0.000
## 114 chla_1 ~ clad_1 0.000 0.000 0.000 0.000 0.000
## 115 chla_1 ~ hcope_1 0.000 0.000 0.000 0.000 0.000
## 116 chla_1 ~ corbic_1 0.000 0.000 0.000 0.000 0.000
## 117 clad_1 ~ ammonia 0.070 -0.062 -0.062 -0.059 -0.059
## 118 clad_1 ~ dophos 0.035 0.031 0.031 0.036 0.036
## 119 clad_1 ~ chla 0.854 -0.260 -0.260 -0.282 -0.282
## 120 clad_1 ~ hcope 0.052 -0.017 -0.017 -0.012 -0.012
## 121 clad_1 ~ clad 0.885 -0.147 -0.147 -0.147 -0.147
## 122 clad_1 ~ corbic 1.042 0.039 0.039 0.050 0.050
## 123 clad_1 ~ flow_1 0.000 0.000 0.000 0.000 0.000
## 124 clad_1 ~ chla_1 0.000 0.000 0.000 0.000 0.000
## 125 clad_1 ~ hcope_1 0.000 0.000 0.000 0.000 0.000
## 126 clad_1 ~ corbic_1 0.000 0.000 0.000 0.000 0.000
## 127 hcope_1 ~ ammonia 0.044 0.008 0.008 0.010 0.010
## 128 hcope_1 ~ dophos 0.297 -0.015 -0.015 -0.024 -0.024
## 129 hcope_1 ~ chla 0.018 -0.014 -0.014 -0.020 -0.020
## 130 hcope_1 ~ hcope 0.755 0.123 0.123 0.116 0.116
## 131 hcope_1 ~ clad 2.512 -0.053 -0.053 -0.070 -0.070
## 132 hcope_1 ~ corbic 0.231 -0.013 -0.013 -0.022 -0.022
## 133 hcope_1 ~ flow_1 0.000 0.000 0.000 0.000 0.000
## 134 hcope_1 ~ chla_1 0.000 0.000 0.000 0.000 0.000
## 135 hcope_1 ~ clad_1 0.000 0.000 0.000 0.000 0.000
## 136 hcope_1 ~ corbic_1 0.000 0.000 0.000 0.000 0.000
## 137 corbic_1 ~ ammonia 0.979 0.076 0.076 0.057 0.057
## 138 corbic_1 ~ dophos 1.972 -0.078 -0.078 -0.073 -0.073
## 139 corbic_1 ~ chla 0.125 -0.072 -0.072 -0.061 -0.061
## 140 corbic_1 ~ hcope 0.056 0.023 0.023 0.013 0.013
## 141 corbic_1 ~ clad 0.154 0.025 0.025 0.020 0.020
## 142 corbic_1 ~ corbic 0.243 -0.152 -0.152 -0.153 -0.153
## 143 corbic_1 ~ flow_1 0.000 0.000 0.000 0.000 0.000
## 144 corbic_1 ~ chla_1 0.000 0.000 0.000 0.000 0.000
## 145 corbic_1 ~ clad_1 0.000 0.000 0.000 0.000 0.000
## 146 corbic_1 ~ hcope_1 0.000 0.000 0.000 0.000 0.000labelsnorth=createLabels(modfitN, cnameslag)
myLavaanPlot(model=modfitN, labels=labelsnorth,
node_options=list(shape="box", fontname="Helvetica"),
coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
width=c("regress","latent"),
color=c("regress","latent"))modFW='chla~chla_1+hcope_1+potam_1+amphi_1
hcope~chla_1+hcope_1
potam~chla_1+potam_1
amphi~chla_1+amphi_1
'
modWN='chla~chla_1+hcope_1+potam_1+amphi_1+clad_1
hcope~chla_1+hcope_1
potam~chla_1+potam_1
amphi~chla_1+amphi_1
clad~chla_1+clad_1
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modWN, data=filter(fdr_ds,region=="West"))
modfitN=sem(modWN, data=filter(fdr_ds,region=="North"))
summary(modfitFW, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 25 iterations
##
## Optimization method NLMINB
## Number of free parameters 20
##
## Used Total
## Number of observations 234 311
##
## Estimator ML
## Model Fit Test Statistic 18.231
## Degrees of freedom 6
## P-value (Chi-square) 0.006
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## chla_1 0.237 0.063 3.753 0.000 0.237 0.239
## hcope_1 0.056 0.054 1.037 0.300 0.056 0.067
## potam_1 -0.014 0.045 -0.303 0.762 -0.014 -0.019
## amphi_1 0.076 0.048 1.595 0.111 0.076 0.101
## hcope ~
## chla_1 0.053 0.082 0.645 0.519 0.053 0.039
## hcope_1 0.467 0.069 6.759 0.000 0.467 0.405
## potam ~
## chla_1 0.073 0.069 1.056 0.291 0.073 0.051
## potam_1 0.672 0.049 13.846 0.000 0.672 0.668
## amphi ~
## chla_1 0.042 0.044 0.960 0.337 0.042 0.032
## amphi_1 0.846 0.033 25.440 0.000 0.846 0.855
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla ~~
## .hcope 0.028 0.037 0.741 0.458 0.028 0.049
## .potam 0.031 0.032 0.999 0.318 0.031 0.065
## .amphi -0.003 0.020 -0.170 0.865 -0.003 -0.011
## .hcope ~~
## .potam 0.050 0.041 1.219 0.223 0.050 0.080
## .amphi 0.012 0.026 0.471 0.637 0.012 0.031
## .potam ~~
## .amphi 0.017 0.022 0.766 0.444 0.017 0.050
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.434 0.040 10.817 0.000 0.434 0.916
## .hcope 0.747 0.069 10.817 0.000 0.747 0.830
## .potam 0.533 0.049 10.817 0.000 0.533 0.550
## .amphi 0.215 0.020 10.817 0.000 0.215 0.262
##
## R-Square:
## Estimate
## chla 0.084
## hcope 0.170
## potam 0.450
## amphi 0.738summary(modfitW, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 33 iterations
##
## Optimization method NLMINB
## Number of free parameters 28
##
## Used Total
## Number of observations 257 311
##
## Estimator ML
## Model Fit Test Statistic 29.989
## Degrees of freedom 12
## P-value (Chi-square) 0.003
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## chla_1 0.183 0.060 3.036 0.002 0.183 0.185
## hcope_1 0.072 0.050 1.420 0.156 0.072 0.088
## potam_1 -0.004 0.050 -0.071 0.943 -0.004 -0.005
## amphi_1 0.055 0.044 1.249 0.212 0.055 0.079
## clad_1 0.220 0.054 4.048 0.000 0.220 0.246
## hcope ~
## chla_1 0.067 0.072 0.932 0.351 0.067 0.056
## hcope_1 0.350 0.058 5.998 0.000 0.350 0.353
## potam ~
## chla_1 -0.027 0.052 -0.520 0.603 -0.027 -0.020
## potam_1 0.772 0.038 20.105 0.000 0.772 0.775
## amphi ~
## chla_1 0.000 0.046 0.007 0.995 0.000 0.000
## amphi_1 0.850 0.032 26.321 0.000 0.850 0.853
## clad ~
## chla_1 0.046 0.062 0.731 0.464 0.046 0.041
## clad_1 0.444 0.055 8.090 0.000 0.444 0.444
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla ~~
## .hcope 0.076 0.031 2.459 0.014 0.076 0.155
## .potam 0.002 0.023 0.102 0.919 0.002 0.006
## .amphi 0.014 0.020 0.679 0.497 0.014 0.042
## .clad 0.004 0.027 0.134 0.893 0.004 0.008
## .hcope ~~
## .potam -0.037 0.028 -1.324 0.185 -0.037 -0.083
## .amphi -0.024 0.025 -0.957 0.339 -0.024 -0.060
## .clad -0.064 0.033 -1.932 0.053 -0.064 -0.121
## .potam ~~
## .amphi 0.027 0.018 1.472 0.141 0.027 0.092
## .clad -0.067 0.025 -2.712 0.007 -0.067 -0.172
## .amphi ~~
## .clad 0.003 0.022 0.151 0.880 0.003 0.009
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.406 0.036 11.336 0.000 0.406 0.874
## .hcope 0.596 0.053 11.336 0.000 0.596 0.863
## .potam 0.332 0.029 11.336 0.000 0.332 0.397
## .amphi 0.260 0.023 11.336 0.000 0.260 0.272
## .clad 0.465 0.041 11.336 0.000 0.465 0.797
##
## R-Square:
## Estimate
## chla 0.126
## hcope 0.137
## potam 0.603
## amphi 0.728
## clad 0.203summary(modfitN, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 31 iterations
##
## Optimization method NLMINB
## Number of free parameters 28
##
## Used Total
## Number of observations 255 311
##
## Estimator ML
## Model Fit Test Statistic 60.092
## Degrees of freedom 12
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## chla_1 0.153 0.061 2.488 0.013 0.153 0.154
## hcope_1 0.016 0.092 0.176 0.860 0.016 0.011
## potam_1 -0.072 0.068 -1.054 0.292 -0.072 -0.067
## amphi_1 0.008 0.057 0.138 0.890 0.008 0.009
## clad_1 0.130 0.072 1.811 0.070 0.130 0.121
## hcope ~
## chla_1 -0.013 0.038 -0.342 0.732 -0.013 -0.020
## hcope_1 0.324 0.053 6.063 0.000 0.324 0.349
## potam ~
## chla_1 -0.048 0.048 -1.001 0.317 -0.048 -0.052
## potam_1 0.542 0.050 10.900 0.000 0.542 0.554
## amphi ~
## chla_1 0.082 0.059 1.381 0.167 0.082 0.074
## amphi_1 0.507 0.054 9.365 0.000 0.507 0.504
## clad ~
## chla_1 -0.061 0.051 -1.197 0.231 -0.061 -0.068
## clad_1 0.432 0.053 8.087 0.000 0.432 0.441
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla ~~
## .hcope 0.034 0.025 1.326 0.185 0.034 0.083
## .potam 0.043 0.032 1.346 0.178 0.043 0.085
## .amphi -0.037 0.039 -0.945 0.345 -0.037 -0.059
## .clad 0.034 0.034 0.997 0.319 0.034 0.063
## .hcope ~~
## .potam 0.036 0.020 1.791 0.073 0.036 0.113
## .amphi 0.013 0.025 0.548 0.584 0.013 0.034
## .clad -0.069 0.022 -3.211 0.001 -0.069 -0.205
## .potam ~~
## .amphi 0.003 0.031 0.111 0.911 0.003 0.007
## .clad -0.085 0.027 -3.142 0.002 -0.085 -0.201
## .amphi ~~
## .clad 0.028 0.033 0.860 0.390 0.028 0.054
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.645 0.057 11.292 0.000 0.645 0.949
## .hcope 0.252 0.022 11.292 0.000 0.252 0.879
## .potam 0.396 0.035 11.292 0.000 0.396 0.689
## .amphi 0.612 0.054 11.292 0.000 0.612 0.742
## .clad 0.452 0.040 11.292 0.000 0.452 0.807
##
## R-Square:
## Estimate
## chla 0.051
## hcope 0.121
## potam 0.311
## amphi 0.258
## clad 0.193#par(mfrow=c(1,1))
par(mfrow=c(2,2))
semPaths(modfitFW, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("Far West", line = 1)
semPaths(modfitW, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("West", line = 1)
semPaths(modfitN, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("North", line = 1)
semPaths(modfitS, "std", edge.label.cex = 1, node.width=2, nCharNodes = 0, residuals = F, intercepts = F); title("South", line = 1)
Nice plots
These need to be updated.
Without covariances
Far West
West
North
South